Development of the Rough Set Method to Determine Lecturer Scholarship Opportunities

Authors

Surmayanti , Sarjon Defit

DOI:

10.29303/jppipa.v10i5.7147

Published:

2024-05-25

Issue:

Vol. 10 No. 5 (2024): May

Keywords:

Artificial intelligence, Machine learning, Rough set, Rosetta, Scholarship

Research Articles

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How to Cite

Surmayanti, & Defit, S. (2024). Development of the Rough Set Method to Determine Lecturer Scholarship Opportunities. Jurnal Penelitian Pendidikan IPA, 10(5), 2182–2190. https://doi.org/10.29303/jppipa.v10i5.7147
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Abstract

Currently, all groups can experience the development of artificial intelligence, this happens because artificial intelligence has experienced very significant changes. Artificial Intelligence (AI) consists of several branches, one of which is machine learning. Machine Learning (ML) technology is a branch of AI that is very interesting because it is a machine that can learn like humans. The method used here is the rough set method. In this research, a case will be raised to determine scholarship opportunities for lecturers based on predetermined criteria. To solve the problem above, machine learning was used using the Rough Set method, using Rosetta software. By the regulations determined by the scholarship provider, in this case, the institution concerned where the lecturer is registered as teaching staff to obtain a scholarship, criteria are needed to determine who will be selected to receive the scholarship. The distribution of scholarships is carried out to improve lecturer performance, as an achievement as well as an appreciation for the lecturer concerned for his long service to the institution.

References

Abubakar, A. M., Elrehail, H., Alatailat, M. A., & Elçi, A. (2019). Knowledge management, decision-making style and organizational performance. Journal of Innovation & Knowledge, 4(2), 104–114. https://doi.org/10.1016/j.jik.2017.07.003

Ahmad, S. F., Han, H., Alam, M. M., Rehmat, Mohd. K., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 311. https://doi.org/10.1057/s41599-023-01787-8

Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3

Coman, C., Țîru, L. G., Meseșan-Schmitz, L., Stanciu, C., & Bularca, M. C. (2020). Online Teaching and Learning in Higher Education during the Coronavirus Pandemic: Students’ Perspective. Sustainability, 12(24), 10367. https://doi.org/10.3390/su122410367

Compagnucci, L., & Spigarelli, F. (2020). The Third Mission of the university: A systematic literature review on potentials and constraints. Technological Forecasting and Social Change, 161, 120284. https://doi.org/10.1016/j.techfore.2020.120284

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Fadilah, M., Utari, P., & Wijaya, M. (2022). Government Communication in Implementing Inclusive Education for Working Towards the Sustainable Development Goals. KnE Social Sciences. https://doi.org/10.18502/kss.v7i5.10592

Getie, A. S. (2020). Factors affecting the attitudes of students towards learning English as a foreign language. Cogent Education, 7(1), 1738184. https://doi.org/10.1080/2331186X.2020.1738184

Gurtu, A., & Johny, J. (2021). Supply Chain Risk Management: Literature Review. Risks, 9(1), 16. https://doi.org/10.3390/risks9010016

Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275–285. https://doi.org/10.1016/j.susoc.2022.05.004

Hauge, K. (2021). Self-Study Research: Challenges and Opportunities in Teacher Education. In M. Jose Hernández-Serrano (Ed.), Teacher Education in the 21st Century—Emerging Skills for a Changing World. IntechOpen. https://doi.org/10.5772/intechopen.96252

Huang, K., Wang, J., & Zhang, J. (2023). Automotive Supply Chain Disruption Risk Management: A Visualization Analysis Based on Bibliometric. Processes, 11(3), 710. https://doi.org/10.3390/pr11030710

Ibneatheer, M. U. R., Rostan, P., & Rostan, A. (2023). Internal processes in decision-making (mental, emotional, cultural, ethical and spiritual) of Afghan business leaders. PSU Research Review, 7(1), 33–50. https://doi.org/10.1108/PRR-10-2020-0037

Ideno, T., Morii, M., Takemura, K., & Okada, M. (2020). On Effects of Changing Multi-attribute Table Design on Decision Making: An Eye-Tracking Study. In A.-V. Pietarinen, P. Chapman, L. Bosveld-de Smet, V. Giardino, J. Corter, & S. Linker (Eds.), Diagrammatic Representation and Inference (Vol. 12169, pp. 365–381). Springer International Publishing. https://doi.org/10.1007/978-3-030-54249-8_29

Ishii, Y., Iwao, K., & Kinoshita, T. (2022). A New Rough Set Classifier for Numerical Data Based on Reflexive and Antisymmetric Relations. Machine Learning and Knowledge Extraction, 4(4), 1065–1087. https://doi.org/10.3390/make4040054

Janusz, A., Stawicki, S., Szczuka, M., & Ślęzak, D. (2015). Rough Set Tools for Practical Data Exploration. In D. Ciucci, G. Wang, S. Mitra, & W.-Z. Wu (Eds.), Rough Sets and Knowledge Technology (Vol. 9436, pp. 77–86). Springer International Publishing. https://doi.org/10.1007/978-3-319-25754-9_7

Johnson, P. C., Laurell, C., Ots, M., & Sandström, C. (2022). Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation? Technological Forecasting and Social Change, 179, 121636. https://doi.org/10.1016/j.techfore.2022.121636

Keiler, L. S. (2018). Teachers’ roles and identities in student-centered classrooms. International Journal of STEM Education, 5(1), 34. https://doi.org/10.1186/s40594-018-0131-6

Kim, S., Raza, M., & Seidman, E. (2019). Improving 21st-century teaching skills: The key to effective 21st-century learners. Research in Comparative and International Education, 14(1), 99–117. https://doi.org/10.1177/1745499919829214

Kristanto, S. P., Bahtiar, R. S., Sembiring, M., Himawan, H., Samboteng, L., Hariyadi, & Suparya, I. K. (2021). Implementation of ML Rough Set in Determining Cases of Timely Graduation of Students. Journal of Physics: Conference Series, 1933(1), 012031. https://doi.org/10.1088/1742-6596/1933/1/012031

Kusters, M., Van Der Rijst, R., De Vetten, A., & Admiraal, W. (2023). University lecturers as change agents: How do they perceive their professional agency? Teaching and Teacher Education, 127, 104097. https://doi.org/10.1016/j.tate.2023.104097

Li, L., Wang, Y., Xu, Y., & Lin, K.-Y. (2022). Meta-learning based industrial intelligence of feature nearest algorithm selection framework for classification problems. Journal of Manufacturing Systems, 62, 767–776. https://doi.org/10.1016/j.jmsy.2021.03.007

Lin, G., Xie, L., Li, J., Chen, J., & Kou, Y. (2023). Local double quantitative fuzzy rough sets over two universes. Applied Soft Computing, 145, 110556. https://doi.org/10.1016/j.asoc.2023.110556

Lutfi, M., & Aris, I. (2012). Inconsistent Decision System: Rough Set Data Mining Strategy to Extract Decision Algorithm of a Numerical Distance Relay - Tutorial. In A. Karahoca (Ed.), Advances in Data Mining Knowledge Discovery and Applications. InTech. https://doi.org/10.5772/50460

Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91–99. https://doi.org/10.1016/j.gltp.2022.04.020

Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT. Education Sciences, 13(9), 856. https://doi.org/10.3390/educsci13090856

Ong, S. G. T., & Quek, G. C. L. (2023). Enhancing teacher–student interactions and student online engagement in an online learning environment. Learning Environments Research, 26(3), 681–707. https://doi.org/10.1007/s10984-022-09447-5

Păunescu, C., Nikina-Ruohonen, A., & Stukalina, Y. (2022). Fostering Research with Societal Impact in Higher Education Institutions: A Review and Conceptualization. Social Innovation in Higher Education (pp. 153–178). Springer International Publishing. https://doi.org/10.1007/978-3-030-84044-0_8

Rashid, Y., Rashid, A., Warraich, M. A., Sabir, S. S., & Waseem, A. (2019). Case Study Method: A Step-by-Step Guide for Business Researchers. International Journal of Qualitative Methods, 18, 160940691986242. https://doi.org/10.1177/1609406919862424

Rathore, T. S. (2014). Minimal Realizations of Logic Functions Using Truth Table Method with Distributed Simplification. IETE Journal of Education, 55(1), 26–32. https://doi.org/10.1080/09747338.2014.921412

Riana, W. (2023). Evaluation of Serdos SMART Implementation For Lecturer of the Ministry of Marine Affairs and Fisheries. KnE Social Sciences. https://doi.org/10.18502/kss.v8i11.13573

Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Satinet, C., & Fouss, F. (2022). A Supervised Machine Learning Classification Framework for Clothing Products’ Sustainability. Sustainability, 14(3), 1334. https://doi.org/10.3390/su14031334

Savioni, L., Triberti, S., Durosini, I., & Pravettoni, G. (2023). How to make big decisions: A cross-sectional study on the decision making process in life choices. Current Psychology, 42(18), 15223–15236. https://doi.org/10.1007/s12144-022-02792-x

Sellars, M., Fakirmohammad, R., Bui, L., Fishetti, J., Niyozov, S., Reynolds, R., Thapliyal, N., Smith, Y., & Ali, N. (2018). Conversations on Critical Thinking: Can Critical Thinking Find Its Way Forward as the Skill Set and Mindset of the Century? Education Sciences, 8(4), 205. https://doi.org/10.3390/educsci8040205

Seo, E. J., Park, J.-W., & Choi, Y. J. (2020). The Effect of Social Media Usage Characteristics on e-WOM, Trust, and Brand Equity: Focusing on Users of Airline Social Media. Sustainability, 12(4), 1691. https://doi.org/10.3390/su12041691

Taye, M. M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12(5), 91. https://doi.org/10.3390/computers12050091

Willekens, F., Bijak, J., Klabunde, A., & Prskawetz, A. (2017). The science of choice: An introduction. Population Studies, 71(sup1), 1–13. https://doi.org/10.1080/00324728.2017.1376921

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179. https://doi.org/10.1016/j.xinn.2021.100179

Yu, B., Guo, L., & Li, Q. (2019). A characterization of novel rough fuzzy sets of information systems and their application in decision making. Expert Systems with Applications, 122, 253–261. https://doi.org/10.1016/j.eswa.2019.01.018

Author Biographies

Surmayanti, Universitas Putra Indonesia YPTK Padang

Sarjon Defit, Universitas Putra Indonesia YPTK Padang

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